Soft Computing

, Volume 12, Issue 9, pp 909–918

Tabu search for attribute reduction in rough set theory

Original Paper

DOI: 10.1007/s00500-007-0260-1

Cite this article as:
Hedar, AR., Wang, J. & Fukushima, M. Soft Comput (2008) 12: 909. doi:10.1007/s00500-007-0260-1

Abstract

In this paper, we consider a memory-based heuristic of tabu search to solve the attribute reduction problem in rough set theory. The proposed method, called tabu search attribute reduction (TSAR), is a high-level TS with long-term memory. Therefore, TSAR invokes diversification and intensification search schemes besides the TS neighborhood search methodology. TSAR shows promising and competitive performance compared with some other CI tools in terms of solution qualities. Moreover, TSAR shows a superior performance in saving the computational costs.

Keywords

Computational intelligence Granular computing Attribute reduction Rough set Tabu search 

Copyright information

© Springer-Verlag 2007

Authors and Affiliations

  • Abdel-Rahman Hedar
    • 1
    • 2
  • Jue Wang
    • 1
    • 3
  • Masao Fukushima
    • 1
  1. 1.Department of Applied Mathematics and Physics, Graduate School of InformaticsKyoto UniversityKyotoJapan
  2. 2.Department of Computer Science, Faculty of Computer and Information SciencesAssiut UniversityAssiutEgypt
  3. 3.Academy of Mathematics and System ScienceChinese Academy of ScienceBeijingPeople’s Republic of China

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